About this course
What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in life.
- Overview of AI
- Statistics, Uncertainty, and Bayes networks
- Machine Learning
- Logic and Planning
- Markov Decision Processes and Reinforcement Learning
- Hidden Markov Models and Filters
- Adversarial and Advanced Planning
- Game playing